Tag: ai-strategy
You are at phase zero, and the deck you were sold starts at phase three
Every enterprise AI maturity model starts a rung above where most companies stand and skips the one that holds the rest up: getting the tool safely into people hands. Your team already has Claude. If IT cannot produce the tenant policy, the egress allowlist, the tool allowlist, and the audit log, you are at phase zero, whatever the deck says.
Build vs buy AI - why your leadership does not understand either choice
Companies waste millions choosing build or buy based on cost spreadsheets. RAND Corporation research shows more than 80 percent of AI projects fail at twice the rate of regular IT projects. The real decision is whether your middle managers understand AI well enough to use whatever you build or buy.
The AI tools graveyard: Why 90% fail and how to pick the survivors
Most AI tools will not exist in three years. The economics are brutal: Stanford HAI data shows private investment in generative AI has grown more than eightfold while startups burn through cash twice as fast as a decade ago. Here is how to spot which ones survive and avoid betting your operations on doomed solutions.
The real AI assistant problem no one talks about
Everyone is jumping between ChatGPT, Claude, Gemini, and Perplexity, but constant AI assistant switching costs up to 40 percent of productive time and is destroying the very productivity gains AI was supposed to deliver
Why most AI strategies are venture capital theater
Most AI strategies are elaborate 50-slide performances designed to impress investors and boards. Steve Blank calls this innovation theater. RAND reports more than 80% of AI projects fail because the boring operational work that creates actual value gets ignored.
AI maturity models are broken - here is what works
MIT research shows 95 percent of AI pilots fail to deliver value, yet traditional AI maturity models keep pushing companies through expensive levels. Five contextual factors predict success better than any maturity score.
Scaling AI to enterprise requires unlearning everything
Only 7 percent of organizations fully scale AI past the pilot stage, per MIT Sloan research. The approaches that work for 5 people become liabilities at enterprise scale for 50.
The complete AI implementation checklist
When most AI pilots never reach production, the problem is not the technology. Most AI implementation checklists evaluate features when they should evaluate support, infrastructure readiness, and team preparation.
The executive AI briefing that gets buy-in
Only 25% of AI initiatives deliver expected ROI according to IBM research. Executives approve AI when positioned as business value multipliers with clear ROI timelines and risk controls - not technology experiments
Why your AI readiness assessment is lying to you
Traditional AI readiness assessments measure data quality and infrastructure while missing what actually predicts failure: workflow fragmentation. Knowledge workers already toggle between apps 1,200 times a day and get interrupted every 2 minutes. That is where most AI projects die, not in the data architecture.